The generalized likelihood ratio test (GLRT) is here combined with the nonparametric approach to derive a new adaptive detector for subpixel targets in hyperspectral images. Specifically, a variable bandwidth kernel density estimator (KDE) is employed for estimating the conditional probability density functions composing the GLRT. Although KDE has generally a low mathematical tractability, an approximated closed-form solution is here derived, thanks to an innovative and uncommon choice for the kernel function. Experimental results in subpixel target detection scenarios show that the proposed detector represents not only the natural evolution of but also a successful alternative to both very widely employed and very recently proposed GLRT-based detectors.

中文翻译：

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用于高光谱图像中亚像素目标的封闭形式非参数 GLRT 检测器

广义似然比检验 (GLRT) 在这里与非参数方法相结合，为高光谱图像中的亚像素目标推导出一种新的自适应检测器。具体而言，采用可变带宽核密度估计器 (KDE) 来估计构成 GLRT 的条件概率密度函数。尽管 KDE 通常具有较低的数学易处理性，但由于对核函数进行了创新且不常见的选择，因此这里导出了近似的封闭形式解决方案。在亚像素目标检测场景中的实验结果表明，所提出的检测器不仅代表了非常广泛使用的和最近提出的基于 GLRT 的检测器的自然演变，而且是成功的替代方案。